2,136 research outputs found
Graded quantization for multiple description coding of compressive measurements
Compressed sensing (CS) is an emerging paradigm for acquisition of compressed
representations of a sparse signal. Its low complexity is appealing for
resource-constrained scenarios like sensor networks. However, such scenarios
are often coupled with unreliable communication channels and providing robust
transmission of the acquired data to a receiver is an issue. Multiple
description coding (MDC) effectively combats channel losses for systems without
feedback, thus raising the interest in developing MDC methods explicitly
designed for the CS framework, and exploiting its properties. We propose a
method called Graded Quantization (CS-GQ) that leverages the democratic
property of compressive measurements to effectively implement MDC, and we
provide methods to optimize its performance. A novel decoding algorithm based
on the alternating directions method of multipliers is derived to reconstruct
signals from a limited number of received descriptions. Simulations are
performed to assess the performance of CS-GQ against other methods in presence
of packet losses. The proposed method is successful at providing robust coding
of CS measurements and outperforms other schemes for the considered test
metrics
Compressive Classification
This paper derives fundamental limits associated with compressive
classification of Gaussian mixture source models. In particular, we offer an
asymptotic characterization of the behavior of the (upper bound to the)
misclassification probability associated with the optimal Maximum-A-Posteriori
(MAP) classifier that depends on quantities that are dual to the concepts of
diversity gain and coding gain in multi-antenna communications. The diversity,
which is shown to determine the rate at which the probability of
misclassification decays in the low noise regime, is shown to depend on the
geometry of the source, the geometry of the measurement system and their
interplay. The measurement gain, which represents the counterpart of the coding
gain, is also shown to depend on geometrical quantities. It is argued that the
diversity order and the measurement gain also offer an optimization criterion
to perform dictionary learning for compressive classification applications.Comment: 5 pages, 3 figures, submitted to the 2013 IEEE International
Symposium on Information Theory (ISIT 2013
A New Compressive Video Sensing Framework for Mobile Broadcast
A new video coding method based on compressive
sampling is proposed. In this method, a video is coded using
compressive measurements on video cubes. Video reconstruction
is performed by minimization of total variation (TV) of the pixelwise
discrete cosine transform coefficients along the temporal
direction. A new reconstruction algorithm is developed from
TVAL3, an efficient TV minimization algorithm based on the
alternating minimization and augmented Lagrangian methods.
Video coding with this method is inherently scalable, and has
applications in mobile broadcast
A Comprehensive Review of Distributed Coding Algorithms for Visual Sensor Network (VSN)
Since the invention of low cost camera, it has been widely incorporated into the sensor node in Wireless Sensor Network (WSN) to form the Visual Sensor Network (VSN). However, the use of camera is bringing with it a set of new challenges, because all the sensor nodes are powered by batteries. Hence, energy consumption is one of the most critical issues that have to be taken into consideration. In addition to this, the use of batteries has also limited the resources (memory, processor) that can be incorporated into the sensor node. The life time of a VSN decreases quickly as the image is transferred to the destination. One of the solutions to the aforementioned problem is to reduce the data to be transferred in the network by using image compression. In this paper, a comprehensive survey and analysis of distributed coding algorithms that can be used to encode images in VSN is provided. This also includes an overview of these algorithms, together with their advantages and deficiencies when implemented in VSN. These algorithms are then compared at the end to determine the algorithm that is more suitable for VSN
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